Shama Mogaveera*, Pooja Kolekar and Neha

Department of Computer Science, Visvesvaraya Technological University, Belagavi, India

*Corresponding Author: Shama Mogaveera, Department of Computer Science, Visvesvaraya Technological University, Belagavi, India.

Received: February 25, 2022; Published: March 25, 2022

Abstract

For the past few years, the concept of sentimental analysis has gotten a lot of attention. The collecting of large amounts of data from many sources, the application of appropriate algorithms or approaches, and the classification of the data into various sentiments are the main issues in sentimental analysis. Social media provides a platform in today's fast-paced online environment. Individuals can communicate their feelings in a variety of ways. With the shifting ways of doing things in several sectors of our daily lives, In life, the manner in which one expresses one's viewpoint or perspective has also evolved. People have a natural need to express themselves through writing. regional dialect or in a way that is comfortable for them. Individual evaluations are critical in making judgments. Because of the vast amount of data generated on social media, it's pointless if opinions aren't categorised according to their sentiments. This article includes details on whether the customer's tweets are good, negative, or neutral. To do so, the proposed model scrapes tweets from Twitter utilising Twitter's API. Customer reviews are given distinct emotion scores and classified using APIs, and then text blobs are used to categorise them. Using a text classification model, you can classify something as good, bad, or neutral.

Keywords: Decision-making; Indian languages; Sentimental analysis; Natural Language Processing (NLP); Text Based Classification; Social Media Analysis; Twitter; TextBlob